Assessment of Microbial Air Quality of Nashik City with Particular Reference to Mucorales Fungi, and in Vitro Evaluation of Two Triazole Antifungal Drugs against the Prevalent Mucor Species

Air pollution particularly that of particulate matter (PM 2.5, PM 2.10), carbon monoxide, ozone, nitrogen dioxide, sulfur dioxide, ammonia, lead, and air microbial contaminants, has serious consequences on human health. Air pollution in metros and cities around the world is measured for the above parameters except for the microbial air contaminants. However, microbial air contaminants are important sources of microbial infection in humans and particularly airborne fungi are known to cause diseases like Aspergillosis and Mucormycosis in immunocompromised patients which are about 160 million in the world.In the year 2021, Mucormycosis disease was reported as a post-covid infection in several states of India as a fatal disease caused by a black fungus (Mucor) prevalent in the atmospheric air. In the present study, we assessed the microbial air quality (colony forming unit of microbes/m3 of air) of Nashik city air, in India, for its microbial contaminant, particularly Mucor sp., and further the prevalent Mucor sp. was evaluated for its reaction to two triazole antifungal drugs viz. Itraconazole and Fluconazole available in medical stores.The air quality index of 90 CFU/tidal volume for Mucor species was regarded as safe, based on the studies. Both the triazole drugs at their active ingredient concentration (1000 µg/mL) were unable to check the growth of Mucor fungi. The paper discussed in detail the methods for enumeration of microbial contaminant/m3 of air and in tidal volume

Adaptive importance sampling technique for markov chains using stochastic approximation

For a discrete-time finite-state Markov chain, we develop an adaptive importance sampling scheme to estimate the expected total cost before hitting a set of terminal states. This scheme updates the change of measure at every transition using constant or decreasing step-size stochastic approximation. The updates are shown to concentrate asymptotically in a neighborhood of the desired zero-variance estimator. Through simulation experiments on simple Markovian queues, we observe that the proposed technique performs very well in estimating performance measures related to rare events associated with queue lengths exceeding prescribed thresholds. We include performance comparisons of the proposed algorithm with existing adaptive importance sampling algorithms on some examples. We also discuss the extension of the technique to estimate the infinite horizon expected discounted cost and the expected average cost

Existence and Uniqueness Results for Difference Φ-Laplacian Boundary Value Problems

This paper is devoted to study the existence and uniqueness of solutions to nonlinear difference Φ-Laplacian boundary value problems with mixed and Dirichlet boundary conditions

Fitting in a complex chi^2 landscape using an optimized hypersurface sampling

Fitting a data set with a parametrized model can be seen geometrically as finding the global minimum of the chi^2 hypersurface, depending on a set of parameters {P_i}. This is usually done using the Levenberg-Marquardt algorithm. The main drawback of this algorithm is that despite of its fast convergence, it can get stuck if the parameters are not initialized close to the final solution. We propose a modification of the Metropolis algorithm introducing a parameter step tuning that optimizes the sampling of parameter space. The ability of the parameter tuning algorithm together with simulated annealing to find the global chi^2 hypersurface minimum, jumping across chi^2{P_i} barriers when necessary, is demonstrated with synthetic functions and with real data

A NON OVERLAPPING CAMERA NETWORK: CALIBRATION AND APPLICATION TOWARDS LANE DEPARTURE WARNING

ABSTRACT In this paper, we present a new multi camera approach to Lane Departure Warning (LDW). First, a perspective removal transformation is applied to the camera captured images to convert them into bird's-eye view images. Then, the position of the two cameras relative to a reference point is accurately determined using a new calibration technique. Lane detection is performed on the front and rear camera images who results are combined using data fusion. Finally, LDW is implemented by determining the distance between the vehicle and adjacent lane boundaries. The proposed system was tested on real world driving videos and shows good results when compared to ground truth

Characterization of a bacterial collar and rhizome rot of banana (Musa paradisiaca) caused by strains of Erwinia chrysanthemi pv. paradisiaca

A serious collar and rhizome rot disease of banana was observed in the north region of Maharashtra state in post rainy season. The disease was caused by the bacterial strains of Erwinia chrysanthemi pv. paradisiaca identified and characterized by morphological, physiological, biochemical and pathogenicity tests. The infection occurred on new banana plantation of one month old in poorly drained soil. In post rainy season, banana plantations of 8 to 10 weeks were found severely infected. E. chrysanthemi pv. paradisiaca produced soft rot symptom onhealthy banana rhizomes within three weeks. Two strains were isolated from the collar and rhizome rotted diseased samples which were similar in morphological, physiological and biochemical characteristics, however they differed in the virulence aggressiveness to cause the disease in banana. Strain II caused soft rot symptoms within 19 days, however strain I produced it within 23 days of inoculation with suspension of 3×108 CFU ml-1. The result of this study revealed that strain II was more aggressive as compared to strain I of E. chrysanthemi pv. paradisiaca

Item analysis as tool to validate multiple choice question bank in pharmacology

Background: Multiple choice questions (MCQs) are a common method for formative and summative assessment of medical students. Item analysis enables identifying good MCQs based on difficulty index (DIF I), discrimination index (DI), distracter efficiency (DE). The objective of this study was to assess the quality of MCQs currently in use in pharmacology by item analysis and develop a MCQ bank with quality items.Methods: This cross-sectional study was conducted in 148 second year MBBS students at NKP Salve institute of medical sciences from January 2018 to August 2018. Forty MCQs twenty each from the two term examination of pharmacology were taken for item analysis A correct response to an item was awarded one mark and each incorrect response was awarded zero. Each item was analyzed using Microsoft excel sheet for three parameters such as DIF I, DI, and DE.Results: In present study mean and standard deviation (SD) for Difficulty index (%) Discrimination index (%) and Distractor efficiency (%) were 64.54±19.63, 0.26±0.16 and 66.54±34.59 respectively. Out of 40 items large number of MCQs has acceptable level of DIF (70%) and good in discriminating higher and lower ability students DI (77.5%). Distractor efficiency related to presence of zero or 1 non-functional distrator (NFD) is 80%.Conclusions: The study showed that item analysis is a valid tool to identify quality items which regularly incorporated can help to develop a very useful, valid and a reliable question bank

Knowledge, attitude and practice of adverse drug reactions reporting among nurses in a tertiary care centre

Background: Adverse drug reactions are one of the major medicine related problem related to pharmacotherapy which may lead to increased morbidity and mortality causing increased hospital stay and financial burden on the society. Spontaneous voluntary reporting of adverse drug reaction can play a vital role in generating safety signals in which nurses can play important role, hence this study was undertaken to evaluate the knowledge attitude and practice of ADR reporting along with factors affecting reporting among nurses.Methods: The present study was a cross sectional questionnaire based study, which included nurses of a tertiary care hospital in central India. We tried to find out the possible ways to perk up spontaneous reporting of ADR and factors responsible for scarce reporting of ADRs.Results: After analyzing the data, we observed few of responders were aware of the ADR reporting system and the most encouraging finding was that majority of the responders were of the view that this reporting system is necessary. However, response to practice related questions was below average. Main factors which discouraged ADR reporting by nurses was thinking that reporting would lead to extra work and non availability of forms.Conclusions: The deficiencies in ADR reporting require awareness so as to perquisite spontaneous reporting and improve safety of patients. Training to nurses will lead to improvement in reporting of ADR

Geometrical Insights for Implicit Generative Modeling

Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy criterion. A careful look at the geometries induced by these distances on the space of probability measures reveals interesting differences. In particular, we can establish surprising approximate global convergence guarantees for the $1$-Wasserstein distance,even when the parametric generator has a nonconvex parametrization.Comment: this version fixes a typo in a definitio